Build blueprint
Agent Loop Stopper
A loop controller deciding continue, retry differently, verify, ask, or stop after every step.
- Problem
- Agents continue after diminishing returns, repeat failed actions, or stop before verifying outcomes.
- Why Jev
- Agent loops require many low-latency state judgments where prose is overhead.
- Architecture
- Trace state → progress/failure Scores + next-control Choice → hard iteration budget → agent runtime.
- Current alternative
- Fixed step limits and LLM self-reflection prompts.
- Jev advantage
- Makes control a first-class typed layer and may reduce wasted premium-model calls.
- Unknowns
- Reliable progress detection from partial traces.
Why this confidence: Directly uses cheap decisions inside agent loops, a central Jev-native design space.
This is an authored hypothesis derived from the research corpus. Nothing here demonstrates product demand, or that Jev performs well on this particular workload. Run the validation experiment before building past the MVP.
Sources
Limitations
- Reliable progress detection from partial traces.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Probabilistic predicate + deterministic action PlausibleJev supplies fuzzy predicates while TypeScript, policies, and workflows execute constrained actions.
- Confidence-gated agent control loops Authored HypothesisUse Jev repeatedly to route, verify, and decide whether an agent should continue or escalate.
- Cheap decision calls could make tens or hundreds of semantic judgments per event economical. SpeculativeThe product design follows from published pricing and parallel question primitives, not independent production evidence.
- Parallel decision matrix PlausibleOne state is evaluated against many named questions, replacing repeated prompt/parse cycles.